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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.17446 |
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| _version_ | 1866910759916666880 |
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| author | Ayubinia, Ashraf Woo, Jong-hak Hafezianzadeh, Fatemeh Kim, Taehwan Kim, Changseok |
| author_facet | Ayubinia, Ashraf Woo, Jong-hak Hafezianzadeh, Fatemeh Kim, Taehwan Kim, Changseok |
| contents | In this study, we develop an artificial neural network to estimate the infrared (IR) luminosity and star formation rates (SFR) of galaxies. Our network is trained using 'true' IR luminosity values derived from modeling the IR spectral energy distributions (SEDs) of FIR-detected galaxies. We explore five different sets of input features, each incorporating optical, mid-infrared (MIR), near-infrared (NIR), ultraviolet (UV), and emission line data, along with spectroscopic redshifts and uncertainties. All feature sets yield similar IR luminosity predictions, but including all photometric data leads to slightly improved performance. This suggests that comprehensive photometric information enhances the accuracy of our predictions. Our network is applied to a sample of SDSS galaxies defined as unseen data, and the results are compared with three published catalogs of SFRs. Overall, our network demonstrates excellent performance for star-forming galaxies while we observe discrepancies in composite and AGN samples. These inconsistencies may stem from uncertainties inherent in the compared catalogs or potential limitations in the performance of our network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17446 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prediction of Star Formation Rates Using an Artificial Neural Network Ayubinia, Ashraf Woo, Jong-hak Hafezianzadeh, Fatemeh Kim, Taehwan Kim, Changseok Astrophysics of Galaxies In this study, we develop an artificial neural network to estimate the infrared (IR) luminosity and star formation rates (SFR) of galaxies. Our network is trained using 'true' IR luminosity values derived from modeling the IR spectral energy distributions (SEDs) of FIR-detected galaxies. We explore five different sets of input features, each incorporating optical, mid-infrared (MIR), near-infrared (NIR), ultraviolet (UV), and emission line data, along with spectroscopic redshifts and uncertainties. All feature sets yield similar IR luminosity predictions, but including all photometric data leads to slightly improved performance. This suggests that comprehensive photometric information enhances the accuracy of our predictions. Our network is applied to a sample of SDSS galaxies defined as unseen data, and the results are compared with three published catalogs of SFRs. Overall, our network demonstrates excellent performance for star-forming galaxies while we observe discrepancies in composite and AGN samples. These inconsistencies may stem from uncertainties inherent in the compared catalogs or potential limitations in the performance of our network. |
| title | Prediction of Star Formation Rates Using an Artificial Neural Network |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2412.17446 |